Shao Ji, Cao Jing, Wang Changjun, Xu Peifang, Lou Lixia, Ye Juan
Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China.
Ophthalmol Sci. 2024 Mar 21;4(5):100518. doi: 10.1016/j.xops.2024.100518. eCollection 2024 Sep-Oct.
This study aimed to propose a fully automatic eyelid measurement system and compare the contours of both the upper and lower eyelids of normal individuals according to age and gender.
Prospective study.
Five hundred and forty healthy Chinese aged 0 to 79 years in a tertiary hospital were included.
Facial images in the primary gazing position were used to train and test the proposed automatic system for eye recognition and eye segmentation. According to the 10-millimeter diameter circular marker, measurements were transformed from pixel sizes into factual distances.
Midpupil lid distances (MPLDs) every 15° of all participants were automatically measured in both genders (30 males and 30 females in each age group) by the proposed deep learning (DL)-based system. Intraclass correlation coefficients (ICCs) were performed to assess the agreement between the automatic and manual margin reflex distances (MRDs). The eyelid contour, eyelid asymmetry, and palpebral fissure obliquity were analyzed using MPLD, temporal-versus-nasal MPLD ratio, and the angle between the inner and outer canthi, respectively.
The measurement of MRDs by the automatic system excellently agreed with that of the expert, with ICCs ranging from 0.863 to 0.886. As the age of the participants increased, the values of MPLDs reached a peak in those in their 20s or 30s and then gradually decreased at all angles. The temporal sector showed greater changes in MPLDs than the nasal sector, and the changes were more significant in females than in males. The maximum value of palpebral fissure obliquity appeared before 10 years in both genders and remained relatively stable after the 20s ( > 0.05).
The proposed DL-based eyelid analysis system allowed automatic, accurate, and comprehensive measurement of the eyelid contour. The refinement of eyelid shape quantification could be beneficial for future objective assessment preocular and postocular plastic surgery.
The authors have no proprietary or commercial interest in any materials discussed in this article.
本研究旨在提出一种全自动眼睑测量系统,并根据年龄和性别比较正常个体上下眼睑的轮廓。
前瞻性研究。
纳入一家三级医院的540名年龄在0至79岁的健康中国人。
使用处于初始凝视位置的面部图像来训练和测试所提出的用于眼睛识别和眼睛分割的自动系统。根据直径为10毫米的圆形标记,将测量值从像素大小转换为实际距离。
通过所提出的基于深度学习(DL)的系统,自动测量所有参与者每15°的中瞳孔睑缘距离(MPLD)(每个年龄组30名男性和30名女性)。进行组内相关系数(ICC)分析以评估自动测量与手动测量的边缘反射距离(MRD)之间的一致性。分别使用MPLD、颞侧与鼻侧MPLD比值以及内外眦之间的角度来分析眼睑轮廓、眼睑不对称性和睑裂倾斜度。
自动系统对MRD的测量与专家测量结果高度一致,ICC范围为0.863至0.886。随着参与者年龄的增加,MPLD值在20多岁或30多岁时达到峰值,然后在所有角度逐渐下降。颞侧扇形区域的MPLD变化比鼻侧扇形区域更大,且女性的变化比男性更显著。睑裂倾斜度的最大值在10岁之前在两性中均出现,20岁之后保持相对稳定(>0.05)。
所提出的基于DL的眼睑分析系统能够对眼睑轮廓进行自动、准确和全面的测量。眼睑形状量化的细化可能有利于未来眼前后部整形手术的客观评估。
作者对本文中讨论的任何材料均无所有权或商业利益。